A feature selection approach for unsupervised classification based on clustering

Title: A feature selection approach for unsupervised classification based on clustering
Creator: Rubinov, Alex; Soukhoroukova, Nadejda; Ugon, Julien
Date: 2004
Type: Text; Conference paper
Identifier: http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/38554
Identifier: vital:2739
Abstract: Data have been collected for many years in different scientific (industrial, medical) research groups. Very often these groups kept all the the they could collect. It is possible that the data contains a lot of noisy features which do not bring any information, but make the problem more complicated. The additional study of eliminating non-informative and selecting informative features is very important in the area of Data Mining. There are several feature selection methods which were developed for supervised classification. The area of feature selection for unsupervised classification is not so developed. In this paper we present a new feature selection approach for unsupervised classification, based on clustering and nonsmooth optimisation techniques.
Publisher: University of Ballarat, Ballarat, Victoria : University of Ballarat
Relation: Paper presented at Sixth International Conference on Optimization: Techniques and Applications (ICOTA) , University of Ballarat, Ballarat, Victoria : 9th-11th December 2004
Rights: This metadata is freely available under a CCO license
Subject: 0802 Computation Theory and Mathematics